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Analytic system for graphical interpretability of and improvement of machine learning models

机译:用于机器学习模型的图形可解释性的分析系统

摘要

A computing device provides a cluster connectivity graph presented on a display to summarize machine learning model performance. A classification value is predicted is predicted for a response variable value of each observation vector using a trained model. Observation vectors are divided into overlapping data slices that are separately clustered using the predicted classification value to define a set of clusters. A number of observations in each cluster is computed. An accuracy measure is computed for each cluster based on the predicted classification value. A number of overlapping observations between each pair of clusters is computed. The cluster connectivity graph includes a node for each cluster. A size of each node is determined from the computed number of observations. A fill-pattern of each node is determined from the computed accuracy measure. A connector line between each pair of nodes is determined from the computed number of overlapping observations.
机译:计算设备提供呈现在显示器上的群集连接性图,以总结机器学习模型的性能。使用训练后的模型,预测每个观察向量的响应变量值的分类值。观察向量被分为重叠的数据切片,这些数据切片使用预测的分类值分别进行聚类以定义一组聚类。计算每个聚类中的许多观测值。根据预测的分类值为每个聚类计算准确性度量。计算每对群集之间的许多重叠观测值。群集连接图包括每个群集的一个节点。从计算出的观测值中确定每个节点的大小。从计算出的精度测度确定每个节点的填充模式。从计算出的重叠观测值中确定每对节点之间的连接线。

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